当前位置: X-MOL 学术arXiv.cs.CY › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
arXiv - CS - Computers and Society Pub Date : 2021-06-10 , DOI: arxiv-2106.05498
Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, Suresh Venkatasubramanian

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing algorithms on datasets used in prior work. In many cases, this data is used as a benchmark to demonstrate good performance without accounting for the complexities of criminal justice (CJ) processes. We show that pretrial RAI datasets contain numerous measurement biases and errors inherent to CJ pretrial evidence and due to disparities in discretion and deployment, are limited in making claims about real-world outcomes, making the datasets a poor fit for benchmarking under assumptions of ground truth and real-world impact. Conventional practices of simply replicating previous data experiments may implicitly inherit or edify normative positions without explicitly interrogating assumptions. With context of how interdisciplinary fields have engaged in CJ research, algorithmic fairness practices are misaligned for meaningful contribution in the context of CJ, and would benefit from transparent engagement with normative considerations and values related to fairness, justice, and equality. These factors prompt questions about whether benchmarks for intrinsically socio-technical systems like the CJ system can exist in a beneficial and ethical way.

中文翻译:

它是 COMPASlicated:RAI 数据集和算法公平性基准之间的混乱关系

风险评估工具 (RAI) 数据集,尤其是 ProPublica 的 COMPAS 数据集,由于在先前工作中使用的数据集上比较算法的基准实践,通常用于算法公平性论文。在许多情况下,这些数据被用作证明良好绩效的基准,而无需考虑刑事司法 (CJ) 程序的复杂性。我们表明,审前 RAI 数据集包含 CJ 审前证据固有的许多测量偏差和错误,并且由于自由裁量权和部署的差异,在对真实世界结果的声明方面受到限制,使得数据集不适合在基本事实的假设下进行基准测试和现实世界的影响。简单地复制以前的数据实验的传统做法可能会在不明确询问假设的情况下隐含地继承或启发规范立场。在跨学科领域如何参与 CJ 研究的背景下,算法公平实践在 CJ 背景下无法做出有意义的贡献,并将受益于与公平、正义和平等相关的规范性考虑和价值观的透明参与。这些因素引发了一个问题,即 CJ 系统等本质社会技术系统的基准是否可以以有益和合乎道德的方式存在。并将受益于与公平、正义和平等相关的规范性考虑和价值观的透明参与。这些因素引发了一个问题,即 CJ 系统等本质社会技术系统的基准是否可以以有益和合乎道德的方式存在。并将受益于与公平、正义和平等相关的规范性考虑和价值观的透明参与。这些因素引发了一个问题,即 CJ 系统等本质社会技术系统的基准是否可以以有益和合乎道德的方式存在。
更新日期:2021-06-11
down
wechat
bug